Predictive maintenance of complex system with multi-level reliability structure. Issue 16 (18th August 2017)

• Record Type: Journal Article
• Authors: Lee, Dongjin; Pan, Rong
• Is Part Of: International journal of production research. Volume 55:Issue 16(2017)
• Page Start: 4785
• Page End: 4801
• Publication Date: 2017-08-18
• Subjects: Reliability evaluation -- Markov modelling -- maintenance planning -- Bayesian network -- predictive maintenance; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2017.1299947
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : Onboard sensors, which constantly monitor the states of a system and its components, have made the predictive maintenance (PdM) of a complex system possible. To date, system reliability has been extensively studied with the assumption that systems are either single-component systems or they have a deterministic reliability structure. However, in many realistic problems, there are complex multi-component systems with uncertainties in the system reliability structure. This paper presents a PdM scheme for complex systems by employing discrete time Markov chain models for modelling multiple degradation processes of components and a Bayesian network (BN) model for predicting system reliability. The proposed method can be considered as a special type of dynamic Bayesian network because the same BN is repeatedly used over time for evaluating system reliability and the inter-time–slice connection of the same node is monitored by a sensor. This PdM scheme is able to make probabilistic inference at any system level, so PdM can be scheduled accordingly.